Impact / AI strategy
AI grounded in
real operating value.
I treat AI as a capability to be designed into products and workflows—not a novelty layered onto an unchanged organization.
Adoption framework
Start with the decision or workflow
The strongest use cases begin with a specific customer need, employee task, decision, or bottleneck. From there, teams can determine where generative AI, automation, retrieval, personalization, or predictive insight creates genuine leverage—and where a simpler solution is better.
This keeps adoption anchored in measurable outcomes such as time saved, experience quality, conversion, learning velocity, consistency, and risk reduction.
From pilot to practice
Design the surrounding system
Useful AI requires more than model access. Teams need clear inputs, trusted knowledge, human review, privacy and brand guardrails, evaluation criteria, feedback loops, and ownership. My approach brings product management, experience design, content, data, technology, and governance into the same operating model.
Relevant applications include content and asset workflows, research synthesis, experimentation, customer journey personalization, product discovery, operational automation, and decision support.
Leadership principle
Build confidence through evidence
Small, well-instrumented pilots create the evidence required to scale responsibly. Teams should evaluate usefulness, accuracy, adoption, customer impact, operating cost, and risk—then use what they learn to improve the workflow and expand only where value is demonstrated.